Graph is a data type used to describe the structured objects consisting of nodes and edges, such as citation networks, molecules, and road networks. In graph-related learning and prediction problems, traditional machine learning methods require using feature engineering to process graph data before training or prediction [1]}. In contrast, graph neural networks (GNNs) are proposed to directly operate on graphs to solve graph-related problems in an end-to-end manner [1]}. GNNs exploit neural networks to model graphs and perform representation learning, which requires that GNNs are set with appropriate hyperparameters (e.g., learning rate, the number of neural network layers) to control the learning process. From our perspective, more sophisticated neural architectures are required to process graph data, which may result in challenging HPO tasks for GNNs. Falkner et al. [3]} pointed out that deep learning algorithms are very sensitive to many hyperparameters. The work presented in [4]} demonstrated that HPO for GNNs is vital for achieving satisfactory results in practice. Therefore, research on effective HPO approaches for GNNs is critical and of great value for GNN applied to various real-world problems.
